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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 71–80 of 1918 papers

TitleStatusHype
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationCode1
A Stochastic Game Framework for Efficient Energy Management in Microgrid NetworksCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Deep Active Inference for Partially Observable MDPsCode1
Acting in Delayed Environments with Non-Stationary Markov PoliciesCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
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